Abstract

Biomarkers identification represents one of the most attractive goals in all biological research field due to its important application to improve diagnosis, guide molecularly targeted therapy and monitor activity and therapeutic response across a wide spectrum of disease and pathological states. With its ability to discover new metabolic markers, NMR metabolic profiling (metabonomics), coupled with pattern recognition methods, represents a highly effective approach for disease development and characterization.
The purpose of the present thesis is to explore the recent NMR improvements by applying and developing new metabolomic strategies for biomarkers discovery, including NMR data handling, peaks quantification and fast data acquisition.
We applied NMR spectroscopy and regression techniques to different patient classes to discriminate a) the progressive liver alterations during tumorigenesis and b) the exhaled breath condensate (EBC) of patients with airway diseases.
In the first application a) Principal component analysis (PCA) and Orthogonal Projection to Latent Structures Discriminant Analysis (O2PLS-DA) showed that the disease evolution clearly followed the increase of the lactate together with the remarkable decrease of the glucose signal, thus suggesting that such a signal pattern may act as a potential marker for assessing pathological hepatic lesions. In particular, we identified a statistical model that could be used to distinguish hepatic metastasis and human hepatocarcinoma from a "normal" (healthy) hepatic tissue.
In the second application b) we investigated the role of pre-analytical variables (saliva and disinfectant contamination), potentially influencing EBC, to evaluate the stability and reproducibility of samples and to discriminate healthy subjects from patients with airway disease. Our results show that saliva does not hamper EBC discrimination of different samples, while the disinfectant could act as a strong contaminant. However, by selecting specific non-contaminated regions of spectra, Projection to Latent Structures-Discriminant Analysis (PLS-DA) was able to discriminate EBC of healthy subjects, laryngectomized and patients with chronic obstructive pulmonary disease (COPD).
As a further enhanced tool for high thoughput NMR analysis, we developed and tested a new integration method for 2D NMR spectra quantification, called CAKE, based upon NMR axial symmetry and Monte Carlo Hit-or-Miss technique. Integration tests on simulated and experimental peaks with different degree of overlap, showed the CAKE efficacy in estimating umbiased peak volume, even for strongly overlapping peaks. Moreover, it is substantially independent on digital resolution and SNR.
Finally, we successfully investigated the possibility of exploiting enhanced NMR pulse sequences for fast spectra acquisition. In particular, we applied the so-called SOFAST-HMQC pulse scheme to detect in-cell metabolism. we have applied the SOFAST experiment to 15N-labeled Thalassiosira rotula diatom cells obtaining, to the best of our knowledge, the first application of fast NMR spectroscopy. We collected spectra in 10-15 s of acquisition time, pinpointing the T. rotula 1H-15N metabolic profiling directly in living cells.